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Estimation and hypothesis testing methods in linear models, including t-tests, ANOVA, regression, and multiple regression. Residual analyses, transformations, goodness of fit, interaction and confounding. Implementation in R. Requires a background in calculus, linear algebra, inferential statistics and R computing. Students are required to take GSLL 3095 and GSLL 3096 before taking this class.
MATH 369 (Linear Algebra I); STAT 315 (Intro to Theory and Practice of Statistics); Admission to the Master of Applied Statistics or admission to the Graduate Certificate in Theory and Applications of Regression Models. Written consent of instructor.
Tuition includes access to lecture recordings which are available by streamed video. Lecture recordings may also be available by download or on DVD. To determine viewing options, contact the Department of Statistics degree program staff at stats_ddp@mail.colostate.edu. Visit the Department of Statistics website to learn more about what to do after registration, including creating your eID (if necessary) and accessing your course.
If you have questions about this course or need assistance with an override or permission to register, please contact Graduate and Online Program Coordinator, Alex Peitsmeyer.
Please check the CSU Bookstore for textbook information. Textbook listings are available at the CSU Bookstore about 3 weeks prior to the start of the term.
9704916330 | kirsten.eilertson@colostate.edu
Kirsten Eilertson is originally from Minnesota. She earned her PhD in Statistics from Cornell University. She is an applied statistician, and has previously worked at The Gladstone Institutes at UC San Francisco and Penn State University. Her recent research has focused on modeling the impact of vaccination programs on disease burden in low- and middle-income countries. In her free time you may find her planning her next travel itinerary or hiking with her dog and husband, while optimistically anticipating The Winds of Winter by George R.R. Martin.